{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_gaussian_quantiles\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 生成2维正态分布，生成的数据按分位数分为两类，500个样本,2个样本特征\n",
    "x1, y1 = make_gaussian_quantiles(n_samples=500, n_features=2,n_classes=2)\n",
    "# 生成2维正态分布，生成的数据按分位数分为两类，400个样本,2个样本特征均值都为3\n",
    "x2, y2 = make_gaussian_quantiles(mean=(3, 3), n_samples=500, n_features=2, n_classes=2)\n",
    "# 将两组数据合成一组数据\n",
    "x_data = np.concatenate((x1, x2))\n",
    "y_data = np.concatenate((y1, - y2 + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1dc17276b70>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IQa0x9pW6w4VI4koRknLY2P5YziudljRvKGKGggGEP4AMh5zm1W4L4wWKz7TW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\nUfCKT9xL35YC2Y6IXFeY6NGrdd3aN9zwlofLs2V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EaEcxqDQjLWyqbtQegxUzwF6lmTUR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc0b494ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 决策树模型\n",
    "model = tree.DecisionTreeClassifier(max_depth=3)\n",
    "\n",
    "# 输入数据建立模型\n",
    "model.fit(x_data, y_data)\n",
    "\n",
    "# 获取数据值所在的范围\n",
    "x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "# 生成网格矩阵\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                     np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据\n",
    "z = z.reshape(xx.shape)\n",
    "# 等高线图\n",
    "cs = plt.contourf(xx, yy, z)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.777"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型准确率\n",
    "model.score(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G1NbG184vOEmJTruDZAJvegrJZCAeRzJpvOnpnkZdy5VN0bEjRwlX13Y1BDYs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\ngf2m25zYZPrcefNJEdKSWmmocioumq93czb1NdGupTJjLpM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mJycqVSoI3/3ma9x2V8zv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc17c3ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AdaBoost模型\n",
    "model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),n_estimators=10)\n",
    "# 训练模型\n",
    "model.fit(x_data, y_data)\n",
    "\n",
    "# 获取数据值所在的范围\n",
    "x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "# 生成网格矩阵\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                     np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "# 获取预测值\n",
    "z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "z = z.reshape(xx.shape)\n",
    "# 等高线图\n",
    "cs = plt.contourf(xx, yy, z)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型准确率\n",
    "model.score(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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